Module 4: More Analytics

library(tidyverse)
demographics = read.csv('data/demos_anonymized.csv')
ids = read.csv('data/ids_anonymized.csv')
model_variables = read.csv('data/model_variables_anonymized.csv')

import pandas as pd
import numpy as np
import statsmodels


model_variables = pd.read_csv('data/model_variables_anonymized.csv')

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=1000)  # number of trees

rf_opts = {'max_features': np.arange(2,7)}  # tuning parameter
rf_estimator = GridSearchCV(rf, cv=10, param_grid=rf_opts, n_jobs=4)  # 10-fold cv
results_rf = rf_estimator.fit(X_train, y_train)  # requires matrices

results_rf.best_score_
results_rf.best_params_

rf_predict = results_rf.predict(X_test)
print(metrics.classification_report(y_test, rf_predict))

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